Image processing methods, systems and computer programs are widely used in consumer, commercial and military applications to analyze images. One important aspect of image analysis is image content classification.
In particular, real world images generally contain large amounts of information. A significant portion of this information may only be perceptible to a highly trained expert. In image content classification, information is extracted from an image into components or regions which represent homogeneous elements of information.
For example, Magnetic Resonance Imaging (MRI) of the human brain presents an image content classification challenge. Magnetic resonance images generally contain massive amounts of information which generally requires lengthy interpretation by even the most highly trained human experts. Thus, the development of better image content classification methods, systems and computer programs for MRI can have a major impact on the science of brain development and function, as well as in helping to clarify the neuroanatomical substrates of psychiatric and neurological disorders.
Unfortunately, state-of-the-art computerized tools and software for MRI content classification may lead to error rates exceeding 5-10%, which may not be sufficiently accurate to analyze neuroanatomical and developmental changes of the human brain. Other techniques may be more accurate, but may require lengthy analysis by an expert. For example, advanced image segmentation techniques may use T1-weighted scans, T2-weighted scans, fast spin echo and T1-weighted volume scans. Data may be analyzed using grid-square counting. Unfortunately, although these techniques may have calibrated error of 1-5%, they generally are based on lengthy analysis by an expert. These techniques therefore may be impractical as the volume of available MRI scans become large, and when a statistically significant set of scans are used to draw conclusions of scientific interest and clinical value.
In view of the above, there remains a need for image content classification systems, methods and computer programs which are highly accurate and which may also be performed at high speed, without requiring lengthy expert analysis.